Object recognition and behavioral analysis using semantic reasoning

ABSTRACT

In one embodiment, a device detects an object in an area based on sensor data generated by a plurality of sensors deployed to that area. The device uses a semantic reasoning engine to determine an appearance type of the object, based on the sensor data. The device uses the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data. The device makes, using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object. The device provides an indication of the determination for display.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to object recognition and behavioral analysis using semantic reasoning.

BACKGROUND

Sensor networks are being deployed across a myriad of use cases, to monitor activities in their deployment environments. For instance, security cameras are now ubiquitous in locations such as airports, banks, and the like. Traditionally, review and detection of the activities in an area was left to a user. In the case of security cameras, for example, security personnel may monitor the various video feeds in real time and/or review recorded video, to assess the activities in an area.

Automated object detection and identification are now becoming increasingly feasible, thanks to advances in artificial intelligence. However, there are many instances in which the presumed type of an object is wrong. This can be due to a variety of reasons, ranging from simply a misclassification of the object by the system to more nefarious reasons, such as a malicious actor purposely attempting to obfuscate the actual type of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example hierarchy for a deep fusion reasoning engine (DFRE);

FIG. 4 illustrates an example DFRE architecture;

FIG. 5 illustrates an example of various inference types;

FIG. 6 illustrates an example architecture for multiple DFRE agents;

FIG. 7 illustrates an example DFRE metamodel;

FIG. 8 illustrates an example of using a DFRE metamodel to detect objects and assess their behaviors;

FIG. 9 illustrates an example user interface displaying an object detected in an area; and

FIG. 10 illustrates an example simplified procedure for performance object recognition and behavioral analysis using semantic reasoning.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a device detects an object in an area based on sensor data generated by a plurality of sensors deployed to that area. The device uses a semantic reasoning engine to determine an appearance type of the object, based on the sensor data. The device uses the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data. The device makes, using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object. The device provides an indication of the determination for display.

Description

A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers, cellular phones, workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may be further interconnected by an intermediate network node, such as a router, to forward data from one network to another.

Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless or PLC networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port such as PLC, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100 illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers 110 may be interconnected with provider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order to communicate across a core network, such as an illustrative network backbone 130. For example, routers 110, 120 may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets 140 (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network 100 over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN utilizing a Service Provider network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:

-   1.) Site Type A: a site connected to the network (e.g., via a     private or VPN link) using a single CE router and a single link,     with potentially a backup link (e.g., a 3G/4G/5G/LTE backup     connection). For example, a particular CE router 110 shown in     network 100 may support a given customer site, potentially also with     a backup link, such as a wireless connection. -   2.) Site Type B: a site connected to the network using two MPLS VPN     links (e.g., from different Service Providers) using a single CE     router, with potentially a backup link (e.g., a 3G/4G/5G/LTE     connection). A site of type B may itself be of different types: -   2a.) Site Type B1: a site connected to the network using two MPLS     VPN links (e.g., from different Service Providers), with potentially     a backup link (e.g., a 3G/4G/5G/LTE connection). -   2b.) Site Type B2: a site connected to the network using one MPLS     VPN link and one link connected to the public Internet, with     potentially a backup link (e.g., a 3G/4G/5G/LTE connection). For     example, a particular customer site may be connected to network 100     via PE-3 and via a separate Internet connection, potentially also     with a wireless backup link. -   2c.) Site Type B3: a site connected to the network using two links     connected to the public Internet, with potentially a backup link     (e.g., a 3G/4G/5G/LTE connection). -   Notably, MPLS VPN links are usually tied to a committed service     level agreement, whereas Internet links may either have no service     level agreement or a loose service level agreement (e.g., a “Gold     Package” Internet service connection that guarantees a certain level     of performance to a customer site). -   3.) Site Type C: a site of type B (e.g., types B 1, B2 or B3) but     with more than one CE router (e.g., a first CE router connected to     one link while a second CE router is connected to the other link),     and potentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup     link). For example, a particular customer site may include a first     CE router 110 connected to PE-2 and a second CE router 110 connected     to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail, according to various embodiments. As shown, network backbone 130 may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network 100 may comprise local/branch networks 160, 162 that include devices/nodes 10-16 and devices/nodes 18-20, respectively, as well as a data center/cloud environment 150 that includes servers 152-154. Notably, local networks 160-162 and data center/cloud environment 150 may be located in different geographic locations.

Servers 152-154 may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc.

In various embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects (things) and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks, etc., are often deployed on what are referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for devices/nodes 10-16 in the local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communication challenges. First, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time. Some examples include temporal changes in interference (e.g., other wireless networks or electrical appliances), physical obstructions (e.g., doors opening/closing, seasonal changes such as the foliage density of trees, etc.), and propagation characteristics of the physical media (e.g., temperature or humidity changes, etc.). The time scales of such temporal changes can range between milliseconds (e.g., transmissions from other transceivers) to months (e.g., seasonal changes of an outdoor environment). In addition, LLN devices typically use low-cost and low-power designs that limit the capabilities of their transceivers. In particular, LLN transceivers typically provide low throughput. Furthermore, LLN transceivers typically support limited link margin, making the effects of interference and environmental changes visible to link and network protocols. The high number of nodes in LLNs in comparison to traditional networks also makes routing, quality of service (QoS), security, network management, and traffic engineering extremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 (e.g., an apparatus) that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in FIGS. 1A-1B, particularly the PE routers 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g., a network controller located in a data center, etc.), any other computing device that supports the operations of network 100 (e.g., switches, etc.), or any of the other devices referenced below. The device 200 may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device 200 comprises one or more network interfaces 210, one or more processors 220, and a memory 240 interconnected by a system bus 250, and is powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a deep fusion reasoning engine (DFRE) process 248, as described herein.

It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.

DFRE process 248 includes computer executable instructions that, when executed by processor(s) 220, cause device 200 to provide cognitive reasoning services to a network. In various embodiments, DFRE process 248 may utilize machine learning techniques, in whole or in part, to perform its analysis and reasoning functions. In general, machine learning is concerned with the design and the development of techniques that take as input empirical data (such as network statistics and performance indicators) and recognize complex patterns in these data. One very common pattern among machine learning techniques is the use of an underlying model M, whose hyper-parameters are optimized for minimizing the cost function associated to M, given the input data. The learning process then operates by adjusting the hyper-parameters such that the number of misclassified points is minimal. After this optimization phase (or learning phase), the model M can be used very easily to classify new data points. Often, M is a statistical model, and the minimization of the cost function is equivalent to the maximization of the likelihood function, given the input data.

In various embodiments, DFRE process 248 may employ one or more supervised, unsupervised, or self-supervised machine learning models. Generally, supervised learning entails the use of a training large set of data, as noted above, that is used to train the model to apply labels to the input data. For example, in the case of video recognition and analysis, the training data may include sample video data that depicts a certain object and is labeled as such. On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior. Self-supervised is a representation learning approach that eliminates the pre-requisite requiring humans to label data. Self-supervised learning systems extract and use the naturally available relevant context and embedded metadata as supervisory signals. Self-supervised learning models take a middle ground approach: it is different from unsupervised learning as systems do not learn the inherent structure of data, and it is different from supervised learning as systems learn entirely without using explicitly-provided labels.

Example machine learning techniques that DFRE process 248 can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) artificial neural networks (ANNs) (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like. Accordingly, DFRE process 248 may employ deep learning, in some embodiments. Generally, deep learning is a subset of machine learning that employs ANNs with multiple layers, with a given layer extracting features or transforming the outputs of the prior layer.

The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, the false positives of the model may refer to the number of times the model incorrectly identified an object or condition within a video feed. Conversely, the false negatives of the model may refer to the number of times the model failed to identify an object or condition within a video feed. True negatives and positives may refer to the number of times the model correctly determined that the object or condition was absent in the video or was present in the video, respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.

According to various embodiments, FIG. 3 illustrates an example hierarchy 300 for a deep fusion reasoning engine (DFRE). For example, DFRE process 248 shown in FIG. 2 may execute a DFRE for any number of purposes. In particular, DFRE process 248 may be configured to analyze sensor data in an IoT deployment (e.g., video data, etc.), to analyze networking data for purposes of network assurance, control, enforcing security policies and detecting threats, facilitating collaboration, or, as described in greater detail below, to aid in the development of a collaborative knowledge generation and learning system for visual programming.

In general, a reasoning engine, also known as a ‘semantic reasoner,’ ‘reasoner,’ or ‘rules engine,’ is a specialized form of machine learning software that uses asserted facts or axioms to infer consequences, logically. Typically, a reasoning engine is a form of inference engine that applies inference rules defined via an ontology language. As introduced herein, a DFRE is an enhanced form of reasoning engine that further leverages the power of sub-symbolic machine learning techniques, such as neural networks (e.g., deep learning), allowing the system to operate across the full spectrum of sub-symbolic data all the way to the symbolic level.

At the lowest layer of hierarchy 300 is sub-symbolic layer 302 that processes the sensor data 312 collected from the network. For example, sensor data 312 may include video feed/stream data from any number of cameras located throughout a location. In some embodiments, sensor data 312 may comprise multimodal sensor data from any number of different types of sensors located throughout the location. At the core of sub-symbolic layer 302 may be one or more DNNs 308 or other machine learning-based model that processes the collected sensor data 312. In other words, sub-symbolic layer 302 may perform sensor fusion on sensor data 312 to identify hidden relationships between the data.

At the opposing end of hierarchy 300 may be symbolic layer 306 that may leverage symbolic learning. In general, symbolic learning includes a set of symbolic grammar rules specifying the representation language of the system, a set of symbolic inference rules specifying the reasoning competence of the system, and a semantic theory containing the definitions of “meaning.” This approach differs from other learning approaches that try to establish generalizations from facts as it is about reasoning and extracting knowledge from knowledge. It combines knowledge representations and reasoning to acquire and ground knowledge from observations in a non-axiomatic way. In other words, in sharp contrast to the sub-symbolic learning performed in layer 302, the symbolic learning and generalized intelligence performed at symbolic layer 306 requires a variety of reasoning and learning paradigms that more closely follows how humans learn and are able to explain why a particular conclusion was reached.

Symbolic learning models what are referred to as “concepts,” which comprise a set of properties. Typically, these properties include an “intent” and an “extent,” whereby the intent offers a symbolic way of identifying the extent of the concept. For example, consider the intent that represents motorcycles. The intent for this concept may be defined by properties such as “having two wheels” and “motorized,” which can be used to identify the extent of the concept (e.g., whether a particular vehicle is a motorcycle).

Linking sub-symbolic layer 302 and symbolic layer 306 may be conceptual layer 304 that leverages conceptual spaces. In general, conceptual spaces are a proposed framework for knowledge representation by a cognitive system on the conceptual level that provides a natural way of representing similarities. Conceptual spaces enable the interaction between different type of data representations as an intermediate level between sub-symbolic and symbolic representations.

More formally, a conceptual space is a geometrical structure which is defined by a set of quality dimensions to allow for the measurement of semantic distances between instances of concepts and for the assignment of quality values to their quality dimensions, which correspond to the properties of the concepts. Thus, a point in a conceptual space S may be represented by an n-dimensional conceptual vector v = <d₁, ..., d_(i), ..., d_(n)> where di represents the quality value for the i^(th) quality dimension. For example, consider the concept of taste. A conceptual space for taste may include the following dimensions: sweet, sour, bitter, and salty, each of which may be its own dimension in the conceptual space. The taste of a given food can then be represented as a vector of these qualities in a given space (e.g., ice cream may fall farther along the sweet dimension than that of peanut butter, peanut butter may fall farther along the salty dimension than that of ice cream, etc.). By representing concepts within a geometric conceptual space, similarities can be compared in geometric terms, based on the Manhattan distance between domains or the Euclidean distance within a domain in the space. In addition, similar objects can be grouped into meaningful conceptual space regions through the application of clustering techniques, which extract concepts from data (e.g., observations).

Said differently, a conceptual space is a framework for representing information that models human-like reasoning to compose concepts using other existing concepts. Note that these representations are not competing with symbolic or associationistic representations. Rather, the three kinds can be seen as three levels of representations of cognition with different scales of resolution and complementary. Namely, a conceptual space is built up from geometrical representations based on a number of quality dimensions that complements the symbolic and deep learning models of symbolic layer 306 and sub-symbolic layer 302, representing an operational bridge between them. Each quality dimension may also include any number of attributes, which present other features of objects in a metric subspace based on their measured quality values. Here, similarity between concepts is just a matter of metric distance between them in the conceptual space in which they are embedded.

In other words, a conceptual space is a geometrical representation which allows the discovery of regions that are physically or functionally linked to each other and to abstract symbols used in symbolic layer 306, allowing for the discovery of correlations shared by the conceptual domains during concepts formation. For example, an alert prioritization module may use connectivity to directly acquire and evaluate alerts as evidence. Possible enhancements may include using volume of alerts and novelty of adjacent (spatially / temporally) alerts, to tune level of alertness.

In general, the conceptual space at conceptual layer 304 allows for the discovery of regions that are naturally linked to abstract symbols used in symbolic layer 306. The overall model is bi-directional as it is planned for predictions and action prescriptions depending on the data causing the activation in sub-symbolic layer 302.

Layer hierarchy 300 shown is particularly appealing when matched with the attention mechanism provided by a cognitive system that operates under the assumption of limited resources and time-constraints. For practical applications, the reasoning logic in symbolic layer 306 may be non-axiomatic and constructed around the assumption of insufficient knowledge and resources (AIKR). It may be implemented, for example, with a Non-Axiomatic Reasoning System (open-NARS) 310. However, other reasoning engines can also be used, such as Auto-catalytic Endogenous Reflective Architecture (AERA), OpenCog, and the like, in symbolic layer 306, in further embodiments. Even Prolog may be suitable, in some cases, to implement a reasoning engine in symbolic layer 306. In turn, an output 314 coming from symbolic layer 306 may be provided to a user interface (UI) for review. For example, output 314 may comprise a video feed/stream augmented with inferences or conclusions made by the DFRE, such as the locations of unstocked or under-stocked shelves, etc.

By way of example of symbolic reasoning, consider the ancient Greek syllogism: (1.) All men are mortal, (2.) Socrates is a man, and (3.) therefore, Socrates is mortal. Depending on the formal language used for the symbolic reasoner, these statements can be represented as symbols of a term logic. For example, the first statement can be represented as “man → [mortal]” and the second statement can be represented as “{Socrates} → man.” Thus, the relationship between terms can be used by the reasoner to make inferences and arrive at a conclusion (e.g., “Socrates is mortal”). Non-axiomatic reasoning systems (NARS) generally differ from more traditional axiomatic reasoners in that the former applies a truth value to each statement, based on the amount of evidence available and observations retrieved, while the latter relies on axioms that are treated as a baseline of truth from which inferences and conclusions can be made.

In other words, a DFRE generally refers to a cognitive engine capable of taking sub-symbolic data as input (e.g., raw or processed sensor data regarding a monitored system), recognizing symbolic concepts from that data, and applying symbolic reasoning to the concepts, to draw conclusions about the monitored system.

According to various embodiments, FIG. 4 illustrates an example DFRE architecture 400. As shown, architecture 400 may be implemented across any number of devices or fully on a particular device, as desired. At the core of architecture 400 may be DFRE middleware 402 that offers a collection of services, each of which may have its own interface. In general, DFRE middleware 402 may leverage a library for interfacing, configuring, and orchestrating each service of DFRE middleware 402.

In various embodiments, DFRE middleware 402 may also provide services to support semantic reasoning, such as by an AIKR reasoner. For example, as shown, DFRE middleware 402 may include a NARS agent that performs semantic reasoning for structural learning. In other embodiments, OpenCog or another suitable AIKR semantic reasoner could be used.

One or more DFRE agents 404 may interface with DFRE middleware 402 to orchestrate the various services available from DFRE middleware 402. In addition, DFRE agent 404 may feed and interact with the AIKR reasoner so as to populate and leverage a DFRE knowledge graph with knowledge.

More specifically, in various embodiments, DFRE middleware 402 may obtain sub-symbolic data 408. In turn, DFRE middleware 402 may leverage various ontologies, programs, rules, and/or structured text 410 to translate sub-symbolic data 408 into symbolic data 412 for consumption by DFRE agent 404. This allows DFRE agent 404 to apply symbolic reasoning to symbolic data 412, to populate and update a DFRE knowledge base (KB) 416 with knowledge 414 regarding the problem space (e.g., the network under observation, etc.). In addition, DFRE agent 404 can leverage the stored knowledge 414 in DFRE KB 416 to make assessments/inferences.

For example, DFRE agent 404 may perform semantic graph decomposition on DFRE KB 416 (e.g., a knowledge graph), so as to compute a graph from the knowledge graph of KB 416 that addresses a particular problem. DFRE agent 404 may also perform post-processing on DFRE KB 416, such as performing graph cleanup, applying deterministic rules and logic to the graph, and the like. DFRE agent 404 may further employ a definition of done, to check goals and collect answers using DFRE KB 416.

In general, DFRE KB 416 may comprise any or all of the following:

-   Data -   Ontologies -   Evolutionary steps of reasoning -   Knowledge (e.g., in the form of a knowledge graph)     -   The Knowledge graph also allows different reasoners to:         -   Have their internal subgraphs         -   Share or coalesce knowledge         -   Work cooperatively

In other words, DFRE KB 416 acts as a dynamic and generic memory structure. In some embodiments, DFRE KB 416 may also allow different reasoners to share or coalesce knowledge, have their own internal sub-graphs, and/or work collaboratively in a distributed manner. For example, a first DFRE agent 404 may perform reasoning on a first sub-graph, a second DFRE agent 404 may perform reasoning on a second sub-graph, etc., to evaluate the health of the network and/or find solutions to any detected problems. To communicate with DFRE agent 404, DFRE KB 416 may include a bidirectional Narsese interface or other interface using another suitable grammar.

In various embodiments, DFRE KB 416 can be visualized on a user interface. For example, Cytoscape, which has its building blocks in bioinformatics and genomics, can be used to implement graph analytics and visualizations.

Said differently, DFRE architecture 400 may include any or all of the following the following components:

-   DFRE middleware 402 that comprises:     -   Structural learning component     -   JSON, textual data, ML/DL pipelines, and/or other containerized         services (e.g., using Docker)     -   Hierarchical goal support -   DFRE Knowledge Base (KB) 416 that supports:     -   Bidirectional Narseseese interface     -   Semantic graph decomposition algorithms     -   Graph analytics     -   Visualization services -   DFRE Agent 404     -   DFRE Control System

More specifically, in some embodiments, DFRE middleware 402 may include any or all of the following:

-   Subsymbolic services:     -   Data services to collect sub-symbolic data for consumption -   Reasoner(s) for structural learning -   NARS -   OpenCog -   Optimized hierarchical goal execution     -   Probabilistic programming     -   Causal inference engines -   Visualization Services (e.g., Cytoscape, etc.)

DFRE middleware 402 may also allow the addition of new services needed by different problem domains.

During execution, DFRE agent 404 may, thus, perform any or all of the following:

-   Orchestration of services -   Focus of attention     -   Semantic graph decomposition         -   Addresses combinatorial issues via an automated divide and             conquer approach that works even in non-separable problems             because the overall knowledge graph 416 may allow for             overlap. -   Feeding and interacting with the AIKR reasoner via bidirectional     translation layer to the DFRE knowledge graph.     -   Call middleware services -   Post processing of the graph     -   Graph clean-up     -   Apply deterministic rules and logic to the graph -   Definition of Done (DoD)     -   Check goals and collect answers

FIG. 5 illustrates an example 500 showing the different forms of structural learning that the DFRE framework can employ. More specifically, the inference rules in example 500 relate premises S→M and M→P, leading to a conclusion S→P. Using these rules, the structural learning herein can be implemented using an ontology with respect to an Assumption of Insufficient Knowledge and Resources (AIKR) reasoning engine, as noted previously. This allows the system to rely on finite processing capacity in real time and be prepared for unexpected tasks. More specifically, as shown, the DFRE may support any or all of the following:

-   Syllogistic Logic     -   Logical quantifiers -   Various Reasoning Types     -   Deduction Induction     -   Abduction     -   Induction     -   Revision -   Different Types of Inference -   Local inference -   Backward inference

To address combinatorial explosion, the DFRE knowledge graph may be partitioned such that each partition is processed by one or more DFRE agents 404, as shown in FIG. 6 , in some embodiments. More specifically, any number of DFRE agents 404 (e.g., a first DFRE agent 404 a through an N^(th) DFRE agent 404 n) may be executed by devices connected via a network 602 or by the same device. In some embodiments, DFRE agents 404 a-404 n may be deployed to different platforms (e.g., platforms 604 a-604 n) and/or utilize different learning approaches. For instance, DFRE agent 404 a may leverage neural networks 606, DFRE agent 404 b may leverage Bayesian learning 608, DFRE agent 404 c may leverage statistical learning, and DFRE agent 404 n may leverage decision tree learning 612.

As would be appreciated, graph decomposition can be based on any or all of the following:

-   Spatial relations - for instance, this could include the vertical     industry of a customer, physical location (country) of a network,     scale of a network deployment, or the like. -   Descriptive properties, such as severity, service impact, next step,     etc. -   Graph-based components (isolated subgraphs, minimum spanning trees,     all shortest paths, strongly connected components ...)

Any new knowledge and related reasoning steps can also be input back to the knowledge graph, in various embodiments.

In further embodiments, the DFRE framework may also support various user interface functions, so as to provide visualizations, actions, etc. to the user. To do so, the framework may leverage Cytoscape, web services, or any other suitable mechanism.

At the core of the techniques herein is a knowledge representation metamodel 700 for different levels of abstraction, as shown in FIG. 7 , according to various embodiments. In various embodiments, the DFRE knowledge graph groups information into four different levels, which are labeled L₀, L₁, L₂, and L* and represent different levels of abstraction, with L₀ being closest to raw data coming in from various sensors and external systems and L₂ representing the highest levels of abstraction typically obtained via mathematical means such as statistical learning and reasoning. L* can be viewed as the layer where high-level goals and motivations are stored. The overall structure of this knowledge is also based on anti-symmetric and symmetric relations.

One key advantage of the DFRE knowledge graph is that human level domain expertise, ontologies, and goals are entered at the L₂ level. This leads, by definition, to an unprecedented ability to generalize at the L₂ level thus minimizing the manual effort required to ingest domain expertise.

More formally:

-   L* represents the overall status of the abstraction. In case of a     problem, it triggers problem solving in lower layers via a DFRE     agent 702. -   L_(2.1)-L_(2.∞) = Higher level representations of the world in which     most of concepts and relations are collapsed into simpler     representations. The higher-level representations are     domain-specific representations of lower levels. -   L₁ = has descriptive, teleological and structural information about     L₀. -   L₀ = Object level is the symbolic representation of the physical     world.

In various embodiments, L₂ may comprise both expertise and experience stored in long-term memory, as well as a focus of attention (FOA) in short-term memory. In other words, when a problem is triggered at L*, a DFRE agent 702 that operates on L₂-L₀ may control the FOA so as to focus on different things, in some embodiments.

As would be appreciated, there may be hundreds of thousands or even millions of data points that need to be extracted at L₀. The DFRE’s FOA is based on the abstraction and the DFRE knowledge graph (KG) may be used to keep combinatorial explosion under control.

Said differently, metamodel 700 may generally take the form of a knowledge graph in which semantic knowledge is stored regarding a particular system, such as a computer network and its constituent networking devices. By representing the relationships between such real-world entities (e.g., router A, router B, etc.), as well as their more abstract concepts (e.g., a networking router), DFRE agent 702 can make evaluations regarding the particular system at different levels of extraction. Indeed, metamodel 700 may differ from a more traditional knowledge graph through the inclusion of any or all of the following, in various embodiments:

-   A formal mechanism to represent different levels of abstraction, and     for moving up and down the abstraction hierarchy (e.g., ranging from     extension to intension). -   Additional structure that leverages distinctions/anti-symmetric     relations, as the backbone of the knowledge structures. -   Similarity/symmetric relation-based relations.

As noted above, metamodel 700 can leverage machine learning and semantic reasoning, to make inferences about detected objects from sensor data. A key observation is that object recognition and behavioral analytics are typically entwined whereby the behavior of an object drives its type classification. However, there are situations in which the appearance and its behavior are entirely different. This can be attributable to simply a misclassification of the object, but could also be due to more malicious reasons, such as a malicious actor intentionally attempting to conceal the identity of the object.

Object Recognition and Behavioral Analysis Using Semantic Reasoning

The techniques herein allow for understanding of the behaviors of moving objects using different sensors. This allows the system to detect abnormal events, predict risks, and avoid potentially dangerous situations. In some aspects, the techniques herein leverage semantic reasoning to infer the type of an object at least twice: once based on its appearance and another based on its behavior. If the two inferences do not match, the system may provide an indication (e.g., an alert) for display, to notify a user as to this discrepancy.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the DFRE process 248, which may include computer executable instructions executed by the processor 220 (or independent processor of interfaces 210), to perform functions relating to the techniques described herein.

Specifically, according to various embodiments, a device detects an object in an area based on sensor data generated by a plurality of sensors deployed to that area. The device uses a semantic reasoning engine to determine an appearance type of the object, based on the sensor data. The device uses the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data. The device makes, using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object. The device provides an indication of the determination for display.

Operationally, FIG. 8 illustrates an example 800 of using a DFRE metamodel to detect objects and assess their behaviors, according to various embodiments. Continuing the example of FIG. 7 , at its lower levels, metamodel 700 may be configured to perform object tracking based on sensor data collected from sensors deployed to a particular area. To do so, in some embodiments, metamodel 700 may perform tracklet generation 802 from the captured sensor data.

In general, tracklets may take the form of segments of dynamic trajectories of different objects over time, from the sensor data. To do so, metamodel 700 may include m-number of trackers, as well as a tracklet fusion layer. Such tracklet analysis allows metamodel 700 to also perform spatio-temporal analysis on the sensor data by tracking specific objects found in the video data. For example, in the case of a video feed of a port, a segmenter/deep neural network (DNN) may identify and track an object across a number of different video frames, other sensor measurements, etc., collected from the sensors in the area.

Example systems that may be suitable to implement segmenter/DNN-based detector 406 include YOLOv3, MobileNet, RetinaNet, and the like, to detect classes/objects for tracklets, and DeepMask, Path Aggregation Network (PANet), etc., to discover various regions within a segmentation mask for regionlets of a given video frame. For tracklet generation and fusion, Multiclass multi-object tracker (MC-MOT) has proven to be suitable, during testing.

In various embodiments, tracklet generation 802 may rely on sensor data from any or all of the following: video stream data, radar data, lidar data, sonar data, combinations thereof, or the like. For instance, metamodel 700 may perform any of the following:

-   Radar object tracking 804 - here, the resulting tracklets over time     may be based at least in part on radar data collected from the area. -   Lidar object tracking 806 - the resulting tracklets over time may     also be based at least in part on lidar data collected from the     area. -   Sonar object tracking 808 - the resulting tracklets could also be     based at least in part on sonar data collected from the area, such     as in the case of a port or other area that includes a body of     water.

Based on the above detection and tracking, metamodel 700 may then perform behavior detection 810 and object recognition 812 from the tracklets, using its semantic reasoning engine. In some embodiments, metamodel 700 may do so leveraging a knowledge base 816 that comprises various sets of concepts regarding different types of objects. For instance, the concept of a ‘bird’ object type may include information about the typical appearances of birds, such as their shapes, colors, etc. In addition, the concept of a ‘bird’ may also include information about their typical behaviors, such as a range of speeds/velocities, kinematics, accelerations, decelerations, etc. More specifically, behavior detection 810 may be based on how the object acts over time, whereas recognition 812 may be based on how the object looks.

Note that behavior detection 810 and object recognition 812 may be performed repeatedly over the course of time, such as on an ongoing basis. Thus, it may very well be the case that the behavior and/or appearance of the object may change over time, leading to changes in their type classifications.

In various embodiments, metamodel 700 may also use its semantic reasoning engine to perform reasoning over its knowledge base, to determine whether the identified object types from the appearance and behavior of the object are consistent with one another. For instance, consider the case in which metamodel 700 determine that the probability of the object being a bird, based on its shape/appearance, is 99%, while the probability of the object being a plane, based on its behavior is 95%. Depending on the probabilities of the separate inferences, the differences between the two object types, or other factors, metamodel 700 may determine that an indication of this difference should be presented for display to a user.

In some embodiments, changes in the behavior and/or appearance of the object over time, which may lead to it being re-classified as being of a different type, could also be used to drive notifications. For instance, if a detected sea lion suddenly accelerates to 50 knots, and the top speed of a sea lion is only 30 knots, the system can quickly reassess its conclusions, and determine that a user should be notified.

FIG. 9 illustrates an example user interface 900 displaying an object detected in an area, according to various embodiments. As shown, user interface 900 may include a map 902 of the area under observation, such as an airport, a dock or port, a roadway, an event venue, or the like.

In some embodiments, user interface 900 may include indications 904 that signify various alert levels for the area. For instance, a red portion of map 902 may indicate locations where there is a potential hazard or danger. Similarly, a yellow portion of map 902 may indicate locations where there are warnings. Finally, green portions of map 902 may indicate locations in the area where there are no warnings or alert.

Assume now that there is an object flying in relatively close proximity of an airplane taking off in the area. In such a case, user interface 900 may present an indication 906 of various sensor data collected from the sensors in the area. Here, there are two objects in the area: the airplane, as well as an identified object.

In some embodiments, user interface 900 may also present information 908 of its analysis of the sensor data, for purposes of object recognition. For instance, information 908 may indicate that the system has determined that the shape of the unknown object indicates that it is a bird, but that its behavior is more like an airplane. Thus, even though the object has the appearance of a bird, the system may infer that it is actually a drone flying in the area. As would be appreciated, a drone in the airspace of an airport typically presents a much greater hazard than a bird would.

Based on the discrepancy between the inferred object types for the detected object, user interface 900 may present an indication 910 of its conclusions about the object. More specifically, indication 910 may take the form of an alert that notifies the user that a drone is flying in the runway and could present a potential hazard to the airplane.

In some instances, user interface 900 may also present indication 910 in the form of an overlay on map 902, such as overlay 912. In general, overlay 912 may provide a visual indication of the alert, drawing the attention of the user to the portion of the area to which the alert applies. Clicking such an overlay may, for instance, cause user interface 900 to display additional data about it, such as indication 906, information 908, indication 910, etc.

FIG. 10 illustrates an example simplified procedure 1000 (e.g., a method) for performance object recognition and behavioral analysis using semantic reasoning, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device 200) may perform procedure 1000 by executing stored instructions (e.g., DFRE process 248). The procedure 1000 may start at step 1005, and continues to step 1010, where, as described in greater detail above, the device may detect an object in an area based on sensor data generated by a plurality of sensors deployed to that area. In some embodiments, the sensor data may comprise one or more video feeds. In further embodiments, the sensor data may include radar data, lidar data, sonar data, combinations thereof, or the like.

At step 1015, as detailed above, the device may use a semantic reasoning engine to determine an appearance type of the object, based on the sensor data. In some embodiments, the device may form tracklets from the sensor data, to assess the detected object over time. In turn, the device may leverage a semantic reasoning engine and a knowledge graph of concepts and their relationships, to determine the appearance type of the object. For instance, if the object is flying, has wings, is moving within a certain velocity range, etc., the semantic reasoning engine may infer that the object is a bird.

At step 1020, the device may also use the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data. Similar to the determination of the appearance of the object, the device may also determine a behavioral type of the object. For instance, the device may assess the kinematics, velocity, acceleration, path(s) of travel, etc. of the object, to infer a behavioral type of the object. In some embodiments, the device may also determine that the behavioral type of the object is changing over time. Indeed, some objects may exhibit one type of behavior at a certain point in time and then exhibit a different type of behavior at other points in time.

At step 1025, as detailed above, the device may make, using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object. In some instances, the device may base this decision also in part on a distance between the concepts of the two. For instance, if the appearance of the object is classified to be a seagull, but its behavior is classified as that of an eagle, the device may determine that the two are ‘close enough.’ However, if the appearance of the object is classified to be a seagull, but its behavior is classified as that of a drone, the device may determine that the two do not match. In some embodiments, the device may also base this determination in part on any changing behaviors of the object (e.g., its behavioral type changes between different types over time, etc.).

At step 1030, the device may provide an indication of the determination for display, as described in greater detail above. In some embodiments, the device may also provide an indication as an overlay for a map of the area. In another embodiment, the device may provide the sensor data for display in conjunction with the indication. In various embodiments, the device may provide the indication based on a location of the object (e.g., within a sensitive area), a trajectory of the object (e.g., heading towards a sensitive area), combinations thereof, or the like. For instance, the indication may take the form of an alert regarding the object. Procedure 1000 then ends at step 1035.

It should be noted that while certain steps within procedure 1000 may be optional as described above, the steps shown in FIG. 10 are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein.

The techniques herein, therefore, introduce techniques for object recognition and behavioral analysis using semantic reasoning. In some aspects, the techniques herein can be used to drive alerts, such as when the appearance of an object differs from its behavior, indicating a potentially hazardous condition.

While there have been shown and described illustrative embodiments that provide for object recognition and behavioral analysis using semantic reasoning, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to specific types of sensor systems, the techniques can be extended without undue experimentation to other use cases, as well.

The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein. 

What is claimed is:
 1. A method comprising: detecting, by a device, an object in an area based on sensor data generated by a plurality of sensors deployed to that area; using, by a device, a semantic reasoning engine to determine an appearance type of the object, based on the sensor data; using, by a device, the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data; making, by the device and using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object; and providing, by the device, an indication of the determination for display.
 2. The method as in claim 1, wherein the sensor data comprises a video feed.
 3. The method as in claim 1, wherein the sensor data comprises one or more of: radar data, lidar data, or sonar data.
 4. The method as in claim 1, wherein the indication is an alert.
 5. The method as in claim 1, wherein the semantic reasoning engine uses a knowledge base to determine the appearance type and the behavioral type of the object.
 6. The method as in claim 1, wherein the device provides an indication of the determination for display, based further on a location of the object in the area.
 7. The method as in claim 1, wherein the device provides an indication of the determination for display, based further on a trajectory of the object in the area.
 8. The method as in claim 1, wherein using the semantic reasoning engine to determine the behavioral type of the object comprises: determining that the behavioral type of the object is changing between different types over time; wherein the device makes the determination that the behavioral type of the object does not match the appearance type of the object based in part on the behavioral type of the object changing between different types over time.
 9. The method as in claim 1, wherein providing the indication of the determination for display comprises: providing the indication as an overlay for a map of the area.
 10. The method as in claim 1, wherein providing the indication of the determination for display comprises: providing the sensor data for display in conjunction with the indication.
 11. An apparatus, comprising: a network interface to communicate with a computer network; a processor coupled to the network interface and configured to execute one or more processes; and a memory configured to store a process that is executed by the processor, the process when executed configured to: detect an object in an area based on sensor data generated by a plurality of sensors deployed to that area; use a semantic reasoning engine to determine an appearance type of the object, based on the sensor data; use the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data; make , using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object; and provide an indication of the determination for display.
 12. The apparatus as in claim 11, wherein the sensor data comprises a video feed.
 13. The apparatus as in claim 11, wherein the sensor data comprises one or more of: radar data, lidar data, or sonar data.
 14. The apparatus as in claim 11, wherein the indication is an alert.
 15. The apparatus as in claim 11, wherein the semantic reasoning engine uses a knowledge base to determine the appearance type and the behavioral type of the object.
 16. The apparatus as in claim 11, wherein the apparatus provides an indication of the determination for display, based further on a location of the object in the area.
 17. The apparatus as in claim 11, wherein the apparatus provides an indication of the determination for display, based further on a trajectory of the object in the area.
 18. The apparatus as in claim 11, wherein the apparatus uses the semantic reasoning engine to determine the behavioral type of the object by: determining that the behavioral type of the object is changing between different types over time; wherein the apparatus makes the determination that the behavioral type of the object does not match the appearance type of the object based in part on the behavioral type of the object changing between different types over time.
 19. The apparatus as in claim 11, wherein the apparatus provides the indication of the determination for display by: providing the indication as an overlay for a map of the area.
 20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: detecting, by a device, an object in an area based on sensor data generated by a plurality of sensors deployed to that area; using, by a device, a semantic reasoning engine to determine an appearance type of the object, based on the sensor data; using, by a device, the semantic reasoning engine to determine a behavioral type of the object, based on the sensor data; making, by the device and using the semantic reasoning engine, a determination that the behavioral type of the object does not match the appearance type of the object; and providing, by the device, an indication of the determination for display. 